Electronic storage mechanisms have enabled accumulation of massive amounts of data. For instance, data that previously required volumes of books for recordation can now be stored electronically without expense of printing paper and with a fraction of space needed for storage of paper. In one particular example, deeds and mortgages that were previously recorded in paper volumes can now be stored electronically. Moreover, advances in sensors and other electronic mechanisms now allow massive amounts of data to be collected in real-time. For instance, GPS systems can determine location of an individual or entity by way of satellites and GPS receivers. Electronic storage devices connected thereto can then be employed to retain locations associated with such systems. Various other sensors and data collection devices can also be utilized for obtainment and storage of data.
Collected data relating to particular contexts and/or applications can be employed in connection with data trending and analysis, and predictions can be made as a function of received and analyzed data. Such prediction is, in fact, human nature, and individuals frequently generate such predictions. For instance, a person traveling between a place of employment and a place of residence can determine that during certain times of day within weekdays traffic conditions are subject to high levels of congestion. Thus, prior to leaving a place of work, an individual can predict when and where he will most likely to be slowed in traffic, and can further predict how long they will be subject to congestion. The individual's predictions can further be a function of other variables, such as weather, whether a day is a holiday, events that are geographically proximate, and the like. Thus, when an individual has access to contextual information and has access to (e.g., by way of memory) historical data, the individual can generate predictions.
Predictive models utilized on computer systems can often produce more accurate predictive results than a human, as computer systems may have access to a substantial amount of data. For instance, a computer application can have access to data that represents traffic patterns over twenty years, whereas an individual may have experienced traffic patterns for less than a year. These predictive models can be quite effective when generating predictions associated with common occurrences.
Predictive models, however, can fail when associated with events that are atypical or surprising. Reasons for failure can include lack of understanding of a situation, lack of contemplation of a situation, infrequency of occurrence of an event, and a variety of other factors. Alerting an individual of a surprising event, however, is more critical than alerting the individual of a typical event, because such individual may very well have predicted the typical event without aid of a predictive application. Developing a methodology for identifying events that one or more users would find surprising can be valuable as users do not need to be alerted about situations that they expect. A system could provide value to users by reasoning about when the information would surprise a user. Moreover, a system that could predict when a user would be surprised in the future would be valuable in giving forewarning to users about future states of the world, giving them time to take action such as finding a new alterative or developing a modified plan.